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The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

Never Quit

Dream

You're supposed to fail

In this special episode of the Security Podcast, O’Reilly’s Ben Lorica talks with Parvez Ahammad, who leads the data science and machine learning efforts at Instart Logic. He has applied machine learning in a variety of domains, most recently to computational neuroscience and security. Lorica and Ahammad discuss the challenges of using machine learning in information security.

Parvez Ahammad on Applying Machine Learning to Security

In an era where fake news is fashionable, data can help distinguish the facts from the fiction. But how do you interrogate a spreadsheet and hold the data to account? And how can journalists use data to find stories? 

The BBC has placed the "interrogation of data" at the heart of its annual plan for 2017/18 and subsequent three-year strategy. Around 12 new staff are being recruited, including data journalists, data scientists, developers, designers, a cartographer, and a first head of statistics who will start in August 2017. 

Data is clearly becoming an increasingly important storytelling tool. But is data journalism new? And is it a specialist skill or can anyone do it? 

In this podcast you will learn:

- the skills you need to be a data journalist
- what makes a good data story
- if you need to know how to code or be good at maths to create stories
- how 'scripting' can be useful when working with data
- how long it takes to learn the spreadsheet skills you need to work on data stories 
- how quickly you can go from idea to publication

"I really hope that sometime in the future we drop the term 'data journalism' because it's just journalism." – Helena Bengtsson

Presenter Daniel Wainwright of the BBC England Data Unit is joined by BBC data journalism editor John Walton, Guardian data projects team editor Helena Bengtsson and Paul Bradshaw who leads the new MA in Data Journalism at Birmingham City University.

Find out more about BBC Academy:
Website – www.bbc.co.uk/academy 
Facebook – www.facebook.com/bbcacademy  
Twitter – www.twitter.com/bbcacademy

Data journalism: More important than ever?

Speaker(s): Steve Crawshaw | Editor's note: We apologise for the poor audio quality of this podcast. How do ordinary citizens become dissidents? As journalist and human rights advocate, Steve Crawshaw has witnessed extraordinary change, everywhere from Prague to Yangon.  He explores what Vaclav Havel called the “power of the powerless”, and the role of creative mischief in achieving surprising change. Steve Crawshaw (@stevecrawshaw) is Senior Advocacy Adviser on Global Thematic Issues, Amnesty International and author of Street Spirit: The Power of Protest and Mischief. Dr Liene Ozoliņa-Fitzgerald is a political sociologist at LSE. She is a co-convener of the Department of Sociology Writing Group for faculty and PhD students. The Ralph Miliband Programme (@RMilibandLSE) is one of LSE's most prestigious lecture series and seeks to advance Ralph Miliband's spirit of free social inquiry.

Does Protest Really Work? [Audio]

This episode of the Data Show marks our 100th episode. This podcast stemmed out of video interviews conducted at O’Reilly’s 2014 Foo Camp. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them. We originally conceived of using those initial conversations to be the basis of a regular series of video interviews. The logistics of studio interviews proved too complicated, but those Foo Camp conversations got us thinking about starting a podcast, and the Data Show was born.

To mark this milestone, my colleague Paco Nathan, co-chair of Jupytercon, turned the tables on me and asked me questions about previous Data Show episodes. In particular, we examined the evolution of key topics covered in this podcast: data science and machine learning, data engineering and architecture, AI, and the impact of each of these areas on businesses and companies. I’m proud of how this show has reached so many people across the world, and I’m looking forward to sharing more conversations in the future.

The Evolution of Data Science, Data Engineering, and AI

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

I listen to this pre-workout or even during workout for motivation! It's by a motivational speaker called Eric Thomas. I seriously seriously recommend checking him out, he does a series on Youtube called TGIM, which is part of my daily ritual! His channel is called "etthehiphoppreacher".

Motivational Speech

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

BEST Workout Motivation - Never Give Up! Vol.1

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

The Evolution of Data Science, Data Engineering, and AI

O'Reilly Radar